Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory151.4 KiB
Average record size in memory160.0 B

Variable types

Text3
Numeric14
Categorical2

Alerts

Acousticness is highly overall correlated with EnergyHigh correlation
Danceability is highly overall correlated with ValenceHigh correlation
Energy is highly overall correlated with Acousticness and 1 other fieldsHigh correlation
Loudness is highly overall correlated with EnergyHigh correlation
Valence is highly overall correlated with DanceabilityHigh correlation
Time_Signature is highly imbalanced (82.8%)Imbalance
Key has 136 (14.0%) zerosZeros
Instrumentalness has 282 (29.1%) zerosZeros
Popularity has 13 (1.3%) zerosZeros

Reproduction

Analysis started2024-09-10 20:53:28.387399
Analysis finished2024-09-10 20:53:46.398121
Duration18.01 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Track
Text

Distinct954
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
2024-09-10T17:53:46.721124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length66
Median length47
Mean length18.160991
Min length2

Characters and Unicode

Total characters17598
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique939 ?
Unique (%)96.9%

Sample

1st rowAbc
2nd rowLet It Be
3rd rowI Want You Back
4th rowCecilia
5th rowSpirit In The Sky
ValueCountFrequency (%)
the 157
 
4.4%
you 126
 
3.5%
love 114
 
3.2%
to 79
 
2.2%
me 75
 
2.1%
i 70
 
2.0%
in 63
 
1.8%
a 52
 
1.5%
my 48
 
1.3%
of 44
 
1.2%
Other values (1120) 2728
76.7%
2024-09-10T17:53:47.171635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2587
 
14.7%
e 1658
 
9.4%
o 1255
 
7.1%
n 953
 
5.4%
a 817
 
4.6%
i 722
 
4.1%
t 680
 
3.9%
r 580
 
3.3%
h 508
 
2.9%
l 489
 
2.8%
Other values (68) 7349
41.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17598
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2587
 
14.7%
e 1658
 
9.4%
o 1255
 
7.1%
n 953
 
5.4%
a 817
 
4.6%
i 722
 
4.1%
t 680
 
3.9%
r 580
 
3.3%
h 508
 
2.9%
l 489
 
2.8%
Other values (68) 7349
41.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17598
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2587
 
14.7%
e 1658
 
9.4%
o 1255
 
7.1%
n 953
 
5.4%
a 817
 
4.6%
i 722
 
4.1%
t 680
 
3.9%
r 580
 
3.3%
h 508
 
2.9%
l 489
 
2.8%
Other values (68) 7349
41.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17598
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2587
 
14.7%
e 1658
 
9.4%
o 1255
 
7.1%
n 953
 
5.4%
a 817
 
4.6%
i 722
 
4.1%
t 680
 
3.9%
r 580
 
3.3%
h 508
 
2.9%
l 489
 
2.8%
Other values (68) 7349
41.8%

Artist
Text

Distinct526
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
2024-09-10T17:53:47.430743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length51
Median length36
Mean length13.581011
Min length1

Characters and Unicode

Total characters13160
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique347 ?
Unique (%)35.8%

Sample

1st rowThe Jackson 5
2nd rowThe Beatles
3rd rowThe Jackson 5
4th rowSimon & Garfunkel
5th rowNorman Greenbaum
ValueCountFrequency (%)
the 167
 
7.3%
94
 
4.1%
john 38
 
1.7%
band 33
 
1.5%
and 27
 
1.2%
paul 23
 
1.0%
elton 16
 
0.7%
simon 15
 
0.7%
barry 15
 
0.7%
bee 14
 
0.6%
Other values (784) 1833
80.6%
2024-09-10T17:53:47.796191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1306
 
9.9%
e 1283
 
9.7%
a 914
 
6.9%
n 909
 
6.9%
r 783
 
5.9%
o 774
 
5.9%
i 718
 
5.5%
t 571
 
4.3%
l 552
 
4.2%
s 506
 
3.8%
Other values (57) 4844
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1306
 
9.9%
e 1283
 
9.7%
a 914
 
6.9%
n 909
 
6.9%
r 783
 
5.9%
o 774
 
5.9%
i 718
 
5.5%
t 571
 
4.3%
l 552
 
4.2%
s 506
 
3.8%
Other values (57) 4844
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1306
 
9.9%
e 1283
 
9.7%
a 914
 
6.9%
n 909
 
6.9%
r 783
 
5.9%
o 774
 
5.9%
i 718
 
5.5%
t 571
 
4.3%
l 552
 
4.2%
s 506
 
3.8%
Other values (57) 4844
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1306
 
9.9%
e 1283
 
9.7%
a 914
 
6.9%
n 909
 
6.9%
r 783
 
5.9%
o 774
 
5.9%
i 718
 
5.5%
t 571
 
4.3%
l 552
 
4.2%
s 506
 
3.8%
Other values (57) 4844
36.8%

Duration
Real number (ℝ)

Distinct249
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228.46852
Minimum76
Maximum1561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T17:53:47.906100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum76
5-th percentile150
Q1184
median215
Q3251
95-th percentile354
Maximum1561
Range1485
Interquartile range (IQR)67

Descriptive statistics

Standard deviation83.070574
Coefficient of variation (CV)0.36359746
Kurtosis77.664266
Mean228.46852
Median Absolute Deviation (MAD)33
Skewness6.0449785
Sum221386
Variance6900.7203
MonotonicityNot monotonic
2024-09-10T17:53:48.011195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
207 17
 
1.8%
233 13
 
1.3%
174 13
 
1.3%
215 12
 
1.2%
212 12
 
1.2%
208 12
 
1.2%
213 12
 
1.2%
203 11
 
1.1%
227 11
 
1.1%
230 10
 
1.0%
Other values (239) 846
87.3%
ValueCountFrequency (%)
76 1
0.1%
77 1
0.1%
80 1
0.1%
87 2
0.2%
91 1
0.1%
96 1
0.1%
99 1
0.1%
115 1
0.1%
116 1
0.1%
118 1
0.1%
ValueCountFrequency (%)
1561 1
0.1%
1008 1
0.1%
646 1
0.1%
645 1
0.1%
582 1
0.1%
539 1
0.1%
523 1
0.1%
516 1
0.1%
501 1
0.1%
491 1
0.1%

Time_Signature
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
4
914 
3
 
50
1
 
3
5
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters969
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 914
94.3%
3 50
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Length

2024-09-10T17:53:48.112194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T17:53:48.194099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4 914
94.3%
3 50
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
4 914
94.3%
3 50
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 969
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 914
94.3%
3 50
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 969
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 914
94.3%
3 50
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 969
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 914
94.3%
3 50
 
5.2%
1 3
 
0.3%
5 2
 
0.2%

Danceability
Real number (ℝ)

HIGH CORRELATION 

Distinct491
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58872673
Minimum0.0942
Maximum0.985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T17:53:48.282101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0942
5-th percentile0.304
Q10.488
median0.6
Q30.698
95-th percentile0.8246
Maximum0.985
Range0.8908
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.15752315
Coefficient of variation (CV)0.26756583
Kurtosis-0.27548805
Mean0.58872673
Median Absolute Deviation (MAD)0.103
Skewness-0.33951829
Sum570.4762
Variance0.024813544
MonotonicityNot monotonic
2024-09-10T17:53:48.389803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.637 9
 
0.9%
0.68 7
 
0.7%
0.665 7
 
0.7%
0.639 6
 
0.6%
0.669 6
 
0.6%
0.541 6
 
0.6%
0.565 6
 
0.6%
0.649 6
 
0.6%
0.671 6
 
0.6%
0.529 5
 
0.5%
Other values (481) 905
93.4%
ValueCountFrequency (%)
0.0942 1
0.1%
0.149 2
0.2%
0.16 1
0.1%
0.164 1
0.1%
0.185 1
0.1%
0.195 1
0.1%
0.203 1
0.1%
0.205 1
0.1%
0.207 1
0.1%
0.212 1
0.1%
ValueCountFrequency (%)
0.985 1
 
0.1%
0.965 1
 
0.1%
0.946 1
 
0.1%
0.925 1
 
0.1%
0.919 1
 
0.1%
0.912 3
0.3%
0.911 2
0.2%
0.908 1
 
0.1%
0.9 1
 
0.1%
0.889 1
 
0.1%

Energy
Real number (ℝ)

HIGH CORRELATION 

Distinct539
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58069455
Minimum0.00532
Maximum0.995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T17:53:48.497903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.00532
5-th percentile0.2478
Q10.428
median0.583
Q30.731
95-th percentile0.9056
Maximum0.995
Range0.98968
Interquartile range (IQR)0.303

Descriptive statistics

Standard deviation0.20207498
Coefficient of variation (CV)0.34798842
Kurtosis-0.57449804
Mean0.58069455
Median Absolute Deviation (MAD)0.151
Skewness-0.14444297
Sum562.69302
Variance0.040834297
MonotonicityNot monotonic
2024-09-10T17:53:48.605905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.673 7
 
0.7%
0.528 7
 
0.7%
0.641 6
 
0.6%
0.644 6
 
0.6%
0.409 5
 
0.5%
0.56 5
 
0.5%
0.532 5
 
0.5%
0.626 4
 
0.4%
0.573 4
 
0.4%
0.492 4
 
0.4%
Other values (529) 916
94.5%
ValueCountFrequency (%)
0.00532 1
0.1%
0.0088 1
0.1%
0.0264 1
0.1%
0.0265 1
0.1%
0.0751 1
0.1%
0.0803 1
0.1%
0.0809 1
0.1%
0.0897 1
0.1%
0.112 1
0.1%
0.116 1
0.1%
ValueCountFrequency (%)
0.995 2
0.2%
0.989 1
0.1%
0.987 1
0.1%
0.98 1
0.1%
0.979 1
0.1%
0.974 1
0.1%
0.969 1
0.1%
0.968 2
0.2%
0.961 1
0.1%
0.957 1
0.1%

Key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2053664
Minimum0
Maximum11
Zeros136
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T17:53:48.696409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.5701547
Coefficient of variation (CV)0.6858604
Kurtosis-1.2930395
Mean5.2053664
Median Absolute Deviation (MAD)3
Skewness-0.015580843
Sum5044
Variance12.746004
MonotonicityNot monotonic
2024-09-10T17:53:48.779503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 136
14.0%
7 118
12.2%
9 115
11.9%
2 100
10.3%
5 95
9.8%
4 81
8.4%
1 73
7.5%
10 64
6.6%
11 64
6.6%
8 52
 
5.4%
Other values (2) 71
7.3%
ValueCountFrequency (%)
0 136
14.0%
1 73
7.5%
2 100
10.3%
3 25
 
2.6%
4 81
8.4%
5 95
9.8%
6 46
 
4.7%
7 118
12.2%
8 52
 
5.4%
9 115
11.9%
ValueCountFrequency (%)
11 64
6.6%
10 64
6.6%
9 115
11.9%
8 52
5.4%
7 118
12.2%
6 46
 
4.7%
5 95
9.8%
4 81
8.4%
3 25
 
2.6%
2 100
10.3%

Loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct907
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.8814272
Minimum-31.646
Maximum-2.34
Zeros0
Zeros (%)0.0%
Negative969
Negative (%)100.0%
Memory size15.1 KiB
2024-09-10T17:53:48.875598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-31.646
5-th percentile-15.7326
Q1-12.36
median-9.566
Q3-7.111
95-th percentile-4.6884
Maximum-2.34
Range29.306
Interquartile range (IQR)5.249

Descriptive statistics

Standard deviation3.7234125
Coefficient of variation (CV)-0.37680918
Kurtosis2.6759072
Mean-9.8814272
Median Absolute Deviation (MAD)2.588
Skewness-0.94540047
Sum-9575.103
Variance13.863801
MonotonicityNot monotonic
2024-09-10T17:53:48.979497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-12.472 3
 
0.3%
-10.834 2
 
0.2%
-13.119 2
 
0.2%
-4.653 2
 
0.2%
-9.138 2
 
0.2%
-7.246 2
 
0.2%
-12.923 2
 
0.2%
-10.518 2
 
0.2%
-8.752 2
 
0.2%
-8.555 2
 
0.2%
Other values (897) 948
97.8%
ValueCountFrequency (%)
-31.646 1
0.1%
-30 1
0.1%
-27.103 1
0.1%
-27.09 1
0.1%
-26.128 1
0.1%
-23.56 1
0.1%
-21.657 1
0.1%
-21.644 1
0.1%
-20.518 1
0.1%
-20.439 1
0.1%
ValueCountFrequency (%)
-2.34 1
0.1%
-2.515 1
0.1%
-2.588 1
0.1%
-2.621 1
0.1%
-2.785 1
0.1%
-3.081 1
0.1%
-3.144 1
0.1%
-3.222 1
0.1%
-3.226 1
0.1%
-3.471 1
0.1%

Mode
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
1
735 
0
234 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters969
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 735
75.9%
0 234
 
24.1%

Length

2024-09-10T17:53:49.079504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T17:53:49.291502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 735
75.9%
0 234
 
24.1%

Most occurring characters

ValueCountFrequency (%)
1 735
75.9%
0 234
 
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 969
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 735
75.9%
0 234
 
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 969
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 735
75.9%
0 234
 
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 969
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 735
75.9%
0 234
 
24.1%

Speechiness
Real number (ℝ)

Distinct453
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06008937
Minimum0.0232
Maximum0.737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T17:53:49.372594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0232
5-th percentile0.02674
Q10.0313
median0.0383
Q30.0568
95-th percentile0.1806
Maximum0.737
Range0.7138
Interquartile range (IQR)0.0255

Descriptive statistics

Standard deviation0.065842021
Coefficient of variation (CV)1.0957349
Kurtosis25.310909
Mean0.06008937
Median Absolute Deviation (MAD)0.0095
Skewness4.3845087
Sum58.2266
Variance0.0043351717
MonotonicityNot monotonic
2024-09-10T17:53:49.483500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0336 9
 
0.9%
0.0346 9
 
0.9%
0.0302 8
 
0.8%
0.0287 8
 
0.8%
0.0283 8
 
0.8%
0.0341 8
 
0.8%
0.0369 7
 
0.7%
0.0282 7
 
0.7%
0.0325 7
 
0.7%
0.0298 7
 
0.7%
Other values (443) 891
92.0%
ValueCountFrequency (%)
0.0232 1
 
0.1%
0.0239 1
 
0.1%
0.024 2
0.2%
0.0241 1
 
0.1%
0.0243 2
0.2%
0.0245 1
 
0.1%
0.0246 2
0.2%
0.0247 1
 
0.1%
0.0248 4
0.4%
0.0249 3
0.3%
ValueCountFrequency (%)
0.737 1
0.1%
0.576 1
0.1%
0.467 1
0.1%
0.457 1
0.1%
0.452 1
0.1%
0.448 1
0.1%
0.405 2
0.2%
0.368 1
0.1%
0.364 1
0.1%
0.361 1
0.1%

Acousticness
Real number (ℝ)

HIGH CORRELATION 

Distinct712
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33370655
Minimum2.23 × 10-5
Maximum0.996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T17:53:49.594500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.23 × 10-5
5-th percentile0.006
Q10.0801
median0.272
Q30.544
95-th percentile0.8594
Maximum0.996
Range0.9959777
Interquartile range (IQR)0.4639

Descriptive statistics

Standard deviation0.27987703
Coefficient of variation (CV)0.83869205
Kurtosis-0.84135273
Mean0.33370655
Median Absolute Deviation (MAD)0.2153
Skewness0.59184928
Sum323.36165
Variance0.078331154
MonotonicityNot monotonic
2024-09-10T17:53:49.704502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.357 8
 
0.8%
0.309 5
 
0.5%
0.305 5
 
0.5%
0.484 5
 
0.5%
0.181 5
 
0.5%
0.0185 4
 
0.4%
0.22 4
 
0.4%
0.524 4
 
0.4%
0.122 4
 
0.4%
0.81 4
 
0.4%
Other values (702) 921
95.0%
ValueCountFrequency (%)
2.23 × 10-51
0.1%
0.000109 1
0.1%
0.000133 1
0.1%
0.000215 1
0.1%
0.000261 1
0.1%
0.00028 1
0.1%
0.000288 1
0.1%
0.000385 1
0.1%
0.000598 1
0.1%
0.000668 2
0.2%
ValueCountFrequency (%)
0.996 1
0.1%
0.994 1
0.1%
0.992 1
0.1%
0.983 1
0.1%
0.973 1
0.1%
0.971 1
0.1%
0.965 1
0.1%
0.959 1
0.1%
0.953 1
0.1%
0.95 1
0.1%

Instrumentalness
Real number (ℝ)

ZEROS 

Distinct609
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.046836978
Minimum0
Maximum0.97
Zeros282
Zeros (%)29.1%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T17:53:49.813612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.07 × 10-5
Q30.00275
95-th percentile0.3212
Maximum0.97
Range0.97
Interquartile range (IQR)0.00275

Descriptive statistics

Standard deviation0.16308138
Coefficient of variation (CV)3.4818938
Kurtosis17.744921
Mean0.046836978
Median Absolute Deviation (MAD)5.07 × 10-5
Skewness4.242706
Sum45.385031
Variance0.026595537
MonotonicityNot monotonic
2024-09-10T17:53:49.922614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 282
29.1%
0.000122 4
 
0.4%
1.81 × 10-64
 
0.4%
0.00141 3
 
0.3%
0.00031 3
 
0.3%
0.000171 3
 
0.3%
0.00014 3
 
0.3%
1.1 × 10-62
 
0.2%
0.00192 2
 
0.2%
1.68 × 10-62
 
0.2%
Other values (599) 661
68.2%
ValueCountFrequency (%)
0 282
29.1%
1 × 10-61
 
0.1%
1.08 × 10-61
 
0.1%
1.09 × 10-61
 
0.1%
1.1 × 10-62
 
0.2%
1.2 × 10-61
 
0.1%
1.22 × 10-61
 
0.1%
1.23 × 10-61
 
0.1%
1.28 × 10-61
 
0.1%
1.31 × 10-62
 
0.2%
ValueCountFrequency (%)
0.97 1
0.1%
0.968 1
0.1%
0.963 1
0.1%
0.959 2
0.2%
0.944 1
0.1%
0.94 2
0.2%
0.92 1
0.1%
0.916 1
0.1%
0.912 1
0.1%
0.909 1
0.1%

Liveness
Real number (ℝ)

Distinct527
Distinct (%)54.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17538091
Minimum0.015
Maximum0.985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T17:53:50.026711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.015
5-th percentile0.0481
Q10.0863
median0.119
Q30.197
95-th percentile0.541
Maximum0.985
Range0.97
Interquartile range (IQR)0.1107

Descriptive statistics

Standard deviation0.15421854
Coefficient of variation (CV)0.87933482
Kurtosis6.3748166
Mean0.17538091
Median Absolute Deviation (MAD)0.0433
Skewness2.3733015
Sum169.9441
Variance0.023783358
MonotonicityNot monotonic
2024-09-10T17:53:50.137216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.105 12
 
1.2%
0.108 11
 
1.1%
0.103 10
 
1.0%
0.113 10
 
1.0%
0.122 9
 
0.9%
0.115 9
 
0.9%
0.12 9
 
0.9%
0.114 9
 
0.9%
0.109 8
 
0.8%
0.104 8
 
0.8%
Other values (517) 874
90.2%
ValueCountFrequency (%)
0.015 1
0.1%
0.0166 1
0.1%
0.0188 1
0.1%
0.0199 1
0.1%
0.0295 2
0.2%
0.0309 1
0.1%
0.0318 1
0.1%
0.032 1
0.1%
0.0339 1
0.1%
0.034 1
0.1%
ValueCountFrequency (%)
0.985 1
0.1%
0.974 1
0.1%
0.962 1
0.1%
0.957 1
0.1%
0.935 1
0.1%
0.9 1
0.1%
0.892 1
0.1%
0.805 1
0.1%
0.792 1
0.1%
0.779 1
0.1%

Valence
Real number (ℝ)

HIGH CORRELATION 

Distinct568
Distinct (%)58.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62251312
Minimum1 × 10-5
Maximum0.989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T17:53:50.244214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 10-5
5-th percentile0.1762
Q10.423
median0.652
Q30.846
95-th percentile0.962
Maximum0.989
Range0.98899
Interquartile range (IQR)0.423

Descriptive statistics

Standard deviation0.25223747
Coefficient of variation (CV)0.40519222
Kurtosis-0.9403716
Mean0.62251312
Median Absolute Deviation (MAD)0.206
Skewness-0.40159539
Sum603.21521
Variance0.063623741
MonotonicityNot monotonic
2024-09-10T17:53:50.366067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.962 9
 
0.9%
0.963 8
 
0.8%
0.969 6
 
0.6%
0.971 6
 
0.6%
0.961 5
 
0.5%
0.826 5
 
0.5%
0.718 5
 
0.5%
0.967 5
 
0.5%
0.926 5
 
0.5%
0.89 4
 
0.4%
Other values (558) 911
94.0%
ValueCountFrequency (%)
1 × 10-51
0.1%
0.0346 1
0.1%
0.0348 1
0.1%
0.0385 1
0.1%
0.0393 1
0.1%
0.0397 1
0.1%
0.0558 1
0.1%
0.0579 1
0.1%
0.0589 1
0.1%
0.0685 2
0.2%
ValueCountFrequency (%)
0.989 1
 
0.1%
0.985 1
 
0.1%
0.981 1
 
0.1%
0.979 1
 
0.1%
0.978 1
 
0.1%
0.973 1
 
0.1%
0.972 1
 
0.1%
0.971 6
0.6%
0.97 2
 
0.2%
0.969 6
0.6%

Tempo
Real number (ℝ)

Distinct944
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.87407
Minimum53.986
Maximum211.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T17:53:50.475067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum53.986
5-th percentile79.1642
Q199.975
median117.401
Q3134.004
95-th percentile170.9286
Maximum211.27
Range157.284
Interquartile range (IQR)34.029

Descriptive statistics

Standard deviation27.03621
Coefficient of variation (CV)0.22743573
Kurtosis0.39551209
Mean118.87407
Median Absolute Deviation (MAD)17.074
Skewness0.5854845
Sum115188.97
Variance730.95668
MonotonicityNot monotonic
2024-09-10T17:53:50.585001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102.977 3
 
0.3%
148.063 2
 
0.2%
120.157 2
 
0.2%
166.139 2
 
0.2%
79.764 2
 
0.2%
95.048 2
 
0.2%
132.642 2
 
0.2%
130.166 2
 
0.2%
85.126 2
 
0.2%
103.01 2
 
0.2%
Other values (934) 948
97.8%
ValueCountFrequency (%)
53.986 1
0.1%
61.53 1
0.1%
62.204 1
0.1%
63.059 1
0.1%
65.09 1
0.1%
65.832 1
0.1%
65.861 1
0.1%
67.006 1
0.1%
68.482 1
0.1%
68.69 1
0.1%
ValueCountFrequency (%)
211.27 1
0.1%
207.266 1
0.1%
205.845 1
0.1%
205.747 1
0.1%
203.812 1
0.1%
202.297 1
0.1%
202.14 1
0.1%
201.467 1
0.1%
200.813 1
0.1%
200.423 1
0.1%

Popularity
Real number (ℝ)

ZEROS 

Distinct87
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.623323
Minimum0
Maximum90
Zeros13
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T17:53:50.694011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q144
median56
Q367
95-th percentile78
Maximum90
Range90
Interquartile range (IQR)23

Descriptive statistics

Standard deviation17.989193
Coefficient of variation (CV)0.3354733
Kurtosis0.60997678
Mean53.623323
Median Absolute Deviation (MAD)11
Skewness-0.81688113
Sum51961
Variance323.61107
MonotonicityNot monotonic
2024-09-10T17:53:50.804002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 31
 
3.2%
64 31
 
3.2%
55 30
 
3.1%
49 26
 
2.7%
74 26
 
2.7%
51 25
 
2.6%
71 25
 
2.6%
47 25
 
2.6%
67 25
 
2.6%
62 24
 
2.5%
Other values (77) 701
72.3%
ValueCountFrequency (%)
0 13
1.3%
1 2
 
0.2%
2 2
 
0.2%
3 1
 
0.1%
4 3
 
0.3%
5 2
 
0.2%
6 2
 
0.2%
7 4
 
0.4%
8 1
 
0.1%
9 2
 
0.2%
ValueCountFrequency (%)
90 2
 
0.2%
89 1
 
0.1%
86 3
 
0.3%
85 3
 
0.3%
84 5
0.5%
83 6
0.6%
82 4
 
0.4%
81 9
0.9%
80 10
1.0%
79 4
 
0.4%

Year
Real number (ℝ)

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1974.5707
Minimum1970
Maximum1979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T17:53:50.891429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile1970
Q11972
median1975
Q31977
95-th percentile1979
Maximum1979
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8450711
Coefficient of variation (CV)0.0014408555
Kurtosis-1.2061454
Mean1974.5707
Median Absolute Deviation (MAD)2
Skewness-0.020943093
Sum1913359
Variance8.0944294
MonotonicityIncreasing
2024-09-10T17:53:50.973516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1973 100
10.3%
1974 99
10.2%
1978 99
10.2%
1976 99
10.2%
1977 99
10.2%
1979 98
10.1%
1975 97
10.0%
1972 97
10.0%
1971 93
9.6%
1970 88
9.1%
ValueCountFrequency (%)
1970 88
9.1%
1971 93
9.6%
1972 97
10.0%
1973 100
10.3%
1974 99
10.2%
1975 97
10.0%
1976 99
10.2%
1977 99
10.2%
1978 99
10.2%
1979 98
10.1%
ValueCountFrequency (%)
1979 98
10.1%
1978 99
10.2%
1977 99
10.2%
1976 99
10.2%
1975 97
10.0%
1974 99
10.2%
1973 100
10.3%
1972 97
10.0%
1971 93
9.6%
1970 88
9.1%

Track length
Real number (ℝ)

Distinct56
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.160991
Minimum2
Maximum66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-09-10T17:53:51.062428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q112
median16
Q323
95-th percentile36
Maximum66
Range64
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.4034285
Coefficient of variation (CV)0.51778169
Kurtosis2.5290544
Mean18.160991
Median Absolute Deviation (MAD)5
Skewness1.2748447
Sum17598
Variance88.424468
MonotonicityNot monotonic
2024-09-10T17:53:51.166582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 58
 
6.0%
13 58
 
6.0%
12 55
 
5.7%
15 54
 
5.6%
19 43
 
4.4%
11 43
 
4.4%
18 42
 
4.3%
16 42
 
4.3%
14 41
 
4.2%
17 38
 
3.9%
Other values (46) 495
51.1%
ValueCountFrequency (%)
2 1
 
0.1%
3 7
 
0.7%
4 15
 
1.5%
5 10
 
1.0%
6 11
 
1.1%
7 30
3.1%
8 34
3.5%
9 32
3.3%
10 58
6.0%
11 43
4.4%
ValueCountFrequency (%)
66 1
 
0.1%
62 1
 
0.1%
60 1
 
0.1%
56 3
0.3%
55 1
 
0.1%
54 1
 
0.1%
52 1
 
0.1%
50 1
 
0.1%
49 1
 
0.1%
48 1
 
0.1%
Distinct952
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
2024-09-10T17:53:51.376927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length58
Median length41
Mean length15.373581
Min length0

Characters and Unicode

Total characters14897
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique936 ?
Unique (%)96.6%

Sample

1st rowAbc
2nd rowLet
3rd row Want You Back
4th rowCecilia
5th rowSpirit Sky
ValueCountFrequency (%)
you 126
 
4.9%
love 114
 
4.4%
38
 
1.5%
get 26
 
1.0%
just 18
 
0.7%
like 18
 
0.7%
up 18
 
0.7%
night 17
 
0.7%
woman 17
 
0.7%
way 17
 
0.7%
Other values (1078) 2161
84.1%
2024-09-10T17:53:51.718740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2587
17.4%
e 1348
 
9.0%
o 1061
 
7.1%
a 780
 
5.2%
n 750
 
5.0%
i 683
 
4.6%
t 544
 
3.7%
r 521
 
3.5%
l 459
 
3.1%
s 365
 
2.5%
Other values (67) 5799
38.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14897
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2587
17.4%
e 1348
 
9.0%
o 1061
 
7.1%
a 780
 
5.2%
n 750
 
5.0%
i 683
 
4.6%
t 544
 
3.7%
r 521
 
3.5%
l 459
 
3.1%
s 365
 
2.5%
Other values (67) 5799
38.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14897
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2587
17.4%
e 1348
 
9.0%
o 1061
 
7.1%
a 780
 
5.2%
n 750
 
5.0%
i 683
 
4.6%
t 544
 
3.7%
r 521
 
3.5%
l 459
 
3.1%
s 365
 
2.5%
Other values (67) 5799
38.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14897
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2587
17.4%
e 1348
 
9.0%
o 1061
 
7.1%
a 780
 
5.2%
n 750
 
5.0%
i 683
 
4.6%
t 544
 
3.7%
r 521
 
3.5%
l 459
 
3.1%
s 365
 
2.5%
Other values (67) 5799
38.9%

Interactions

2024-09-10T17:53:44.727480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:29.044110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:30.457322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:31.702808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:32.921974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:34.148281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:35.274493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:36.474844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:37.737842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:38.871504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:40.003865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:41.143147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:42.460161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:43.582564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:44.810501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:29.154376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:30.556750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:31.789897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:33.005023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:34.235282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:35.361772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:36.563843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:37.825938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:38.958395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:40.096750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:41.227471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:42.552163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:43.663840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:44.890508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:29.260600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:30.643655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:31.875357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:33.081968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:34.308282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:35.446772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:36.657233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:37.902847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:39.036394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:40.182969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:41.309333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:42.628161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:43.740840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:44.975477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:29.465593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:30.726613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:31.954289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:33.157978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:34.386284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:35.527773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:36.738329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:37.979020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:39.110440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:40.257979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:41.523489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:42.707159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:43.823839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:45.059500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:29.545595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:30.803918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:32.040449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:33.230033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:34.465284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:35.609771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:36.814235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:38.059116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:39.188395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:40.334510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:41.603484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:42.792161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:43.900837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:45.140507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:29.632599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:30.886161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:32.130949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:33.312036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:34.540279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:35.697235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:36.896908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:38.139269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:39.275701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:40.419612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:41.692485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:42.869161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:43.979839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:45.225504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:29.719921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:30.989162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:32.227984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:33.395066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:34.625282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:35.795418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:36.982909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:38.225272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:39.361699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:40.500819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:41.790487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:42.952770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:44.069840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:45.304836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:29.804923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:31.090160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:32.336951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:33.474071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:34.705386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:35.883418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:37.061161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:38.304273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:39.437703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:40.574914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:41.879628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:43.028769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:44.151848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:45.412849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:29.882923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:31.167159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:32.420640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:33.552016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:34.784662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:35.968245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:37.138158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:38.382268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:39.525927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:40.658910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:41.959923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:43.106770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:44.230844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:45.629788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:29.966926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:31.252164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:32.496739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:33.757067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:34.871667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:36.049345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:37.224161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:38.465143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:39.605927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:40.738906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:42.046919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:43.186770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:44.314851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:45.712749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:30.054378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:31.345400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:32.592878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:33.834981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:34.952656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:36.135653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:37.295158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:38.545141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:39.683836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:40.814078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:42.128926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:43.260770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:44.395784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:45.796840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:30.163812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:31.438704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:32.685491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:33.918090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:35.039659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:36.225656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:37.504858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:38.629171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:39.765837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:40.909116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:42.216921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:43.347307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:44.480105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:45.875756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:30.257902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:31.522919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:32.759466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:33.990066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:35.114658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:36.301605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:37.576844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:38.705282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:39.843933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:40.985396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:42.290920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:43.420313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:44.564532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:45.952786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:30.356481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:31.606887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:32.838560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:34.069281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:35.192481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:36.383656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:37.655846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:38.785278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:39.919837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:41.057402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:42.372922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:43.500566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-10T17:53:44.643508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-10T17:53:51.953631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AcousticnessDanceabilityDurationEnergyInstrumentalnessKeyLivenessLoudnessModePopularitySpeechinessTempoTime_SignatureTrack lengthValenceYear
Acousticness1.000-0.250-0.166-0.563-0.0330.0380.057-0.3700.138-0.120-0.188-0.1060.1850.143-0.211-0.115
Danceability-0.2501.000-0.0160.209-0.0170.008-0.2240.0750.0880.0960.232-0.1110.142-0.1140.5300.122
Duration-0.166-0.0161.0000.0220.1820.052-0.047-0.0500.0990.123-0.042-0.0280.000-0.022-0.0650.161
Energy-0.5630.2090.0221.000-0.000-0.0490.0580.6550.0630.0800.3290.1480.171-0.0910.3960.008
Instrumentalness-0.033-0.0170.182-0.0001.0000.029-0.080-0.1800.1120.003-0.080-0.0540.117-0.013-0.009-0.009
Key0.0380.0080.052-0.0490.0291.0000.069-0.0740.207-0.042-0.001-0.0180.0000.046-0.018-0.040
Liveness0.057-0.224-0.0470.058-0.0800.0691.0000.0790.022-0.0270.025-0.0200.0180.029-0.140-0.008
Loudness-0.3700.075-0.0500.655-0.180-0.0740.0791.0000.0140.1620.1540.0680.413-0.1480.0340.035
Mode0.1380.0880.0990.0630.1120.2070.0220.0141.0000.0820.0410.0000.0510.0000.0000.061
Popularity-0.1200.0960.1230.0800.003-0.042-0.0270.1620.0821.0000.0230.0220.053-0.403-0.0300.136
Speechiness-0.1880.232-0.0420.329-0.080-0.0010.0250.1540.0410.0231.0000.1270.000-0.1450.092-0.055
Tempo-0.106-0.111-0.0280.148-0.054-0.018-0.0200.0680.0000.0220.1271.0000.126-0.0520.0670.034
Time_Signature0.1850.1420.0000.1710.1170.0000.0180.4130.0510.0530.0000.1261.0000.0000.1660.035
Track length0.143-0.114-0.022-0.091-0.0130.0460.029-0.1480.000-0.403-0.145-0.0520.0001.0000.042-0.064
Valence-0.2110.530-0.0650.396-0.009-0.018-0.1400.0340.000-0.0300.0920.0670.1660.0421.000-0.034
Year-0.1150.1220.1610.008-0.009-0.040-0.0080.0350.0610.136-0.0550.0340.035-0.064-0.0341.000

Missing values

2024-09-10T17:53:46.079843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-10T17:53:46.284838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TrackArtistDurationTime_SignatureDanceabilityEnergyKeyLoudnessModeSpeechinessAcousticnessInstrumentalnessLivenessValenceTempoPopularityYearTrack lengthTrack Transformed
0AbcThe Jackson 516240.6820.9263-2.51500.06070.0404000.0000000.19000.860105.9698119703Abc
1Let It BeThe Beatles24340.4430.4030-8.33910.03220.6310000.0000000.11100.410143.4627819709Let
2I Want You BackThe Jackson 517640.4690.5388-13.55910.05750.3050000.0001140.37000.885196.60678197015Want You Back
3CeciliaSimon & Garfunkel17440.7550.8760-8.86710.03620.3570000.0000050.22000.954102.7627619707Cecilia
4Spirit In The SkyNorman Greenbaum24240.6090.6179-7.09110.03070.0994000.0040400.11800.543128.90375197017Spirit Sky
5Love Grows (WHERE My Rosemary Goes)Edison Lighthouse17440.5680.8249-4.61310.02990.4030000.0000000.08550.753108.62573197035Love Grows (WHERE Rosemary Goes)
6The LetterJoe Cocker9140.7210.4968-6.29610.06450.1400000.0001010.10200.18081.49972197010Letter
7The House Of The Rising SunFrijid Pink27130.2950.5849-6.69600.03450.0003850.2180000.09960.228117.20071197027House Rising Sun
8Fire And RainJames Taylor20340.5970.2715-17.29310.03940.7660000.0119000.09330.33876.27171197013Fire Rain
9In The SummertimeMungo Jerry21140.7540.4494-14.01310.06150.7240000.0000000.16200.97382.75171197017Summertime
TrackArtistDurationTime_SignatureDanceabilityEnergyKeyLoudnessModeSpeechinessAcousticnessInstrumentalnessLivenessValenceTempoPopularityYearTrack lengthTrack Transformed
969A Little More LoveOlivia Newton-John20740.7170.4148-14.85510.03640.029000.0094400.08650.494100.17839197918Little More Love
970In The NavyVillage People22540.7590.8897-10.59200.05020.125000.0000000.04100.886126.20138197911Navy
971Mama Can’t Buy You LoveElton John24440.5290.4325-14.24510.03330.524000.0000000.11500.55594.38236197923Mama Can’t Buy You Love
972Goodnight TonightPaul McCartney & Wings26040.7480.6831-9.88500.04660.056600.0006390.08090.943123.38535197917Goodnight Tonight
973We’ve Got TonightBob Seger & The Silver Bullet Band21540.3790.3878-9.28310.02780.757000.0000000.10300.22261.53026197917Got Tonight
975He’s The Greatest DancerSister Sledge37540.7000.8157-9.71100.04400.001150.0012400.09010.837113.24514197924Greatest Dancer
976Don’t Cry Out LoudMelissa Manchester13540.2980.2520-8.95010.03390.901000.0000090.12700.19390.9559197918Cry Out Loud
977When You’re In Love With A Beautiful WomanDr. Hook17440.6650.6638-11.36710.03860.485000.0068200.15700.792110.6567197942Love Beautiful Woman
978I’ll Never Love This Way AgainDionne Warwick17840.4520.4348-8.87010.03990.792000.0139000.16500.247137.7025197930Never Love Way Again
979Dim All The NightsDonna Summer24840.7580.5407-10.91110.03850.055100.0000000.03430.661121.5810197918Dim All Nights